Abstract: ASTE aims to simultaneously extract aspects,their corresponding opinions,and sentiment polarities from text.It is an emerging and challenging task in aspect-level sentiment analysis.Among existing methods,those based on multi-turn machine reading comprehension have effectively achieved sentiment triplet extraction,but they still exhibit certain limitations.Firstly,the single text feature in multi-turn reading comprehension struggles to adapt to specific subtasks.Secondly,the global self-attention mechanism lacks focus on syntactically more important words and assigns higher attention weights to less significant words.To address these issues,this paper proposes an enhanced multi-turn machine reading comprehension(EMT-MRC) method,which designs a bidirectional attention flow in each turn of reading comprehension to construct the interaction between text and questions,thereby obtaining task-specific text representations.Additionally,dependency syntactic relations are integrated into the Transformer encoder,which constrains the model’s attention distribution through dependency distances,thereby enhancing the model’sfocus on the grammatical aspects of sentences.Experiments on two groups of datasets demonstrate the effectiveness of the proposed method.

Key words: Aspect-level sentiment, Triplet extraction, Machine reading comprehension, Dependency syntax, Bidirectional attention flow